Key points are not available for this paper at this time.
Abstract Electronic noses (e‐noses) have become indispensable analytical platforms for gas detection. However, conventional e‐nose systems face significant limitations in portable and wearable implementations due to their bulk and high‐power consumption. Herein, a single‐sensor‐based multifunctional e‐nose system is reported by integrating a micro‐electromechanical system (MEMS) gas sensor with a flexible printed circuit board (FPCB). Specifically, the ZnO‐ZnSnO 3 raspberry‐like microspheres (ZZSRM) are utilized as the gas‐sensitive materials, and the gas selectivity of the sensor is enhanced through a dual‐temperature modulation strategy. Additionally, a gas classification model based on the MiniRocket algorithm has been developed, enabling efficient feature extraction and low‐complexity classification of response signals, thereby satisfying the real‐time processing demands of embedded devices. Moreover, a silent communication method is proposed, which maps breathing frequency to Morse code for information transmission in specific scenarios. Experimental results demonstrate that the wearable system achieves high‐precision classification and concentration prediction for eight volatile organic compounds (VOCs), while simultaneously enabling robust recognition of exhaled signals and instantaneous conversion of Morse code into legible alphabetic characters. By combining the gas sensor with artificial intelligence (AI) technology, this work establishes a multifunctional flexible e‐nose that merges portable gas detection and silent communication, offering a novel technological framework for environmental monitoring and human‐machine interaction.
Xiang et al. (Wed,) studied this question.